# Correlates of milk and milk alternative consumption among Canadian secondary school students, circa 2017/2018

## Acknowledgements

### Authors

Alexandra Butler, MSc1
Kate Battista, PhD(c)1
Scott T. Leatherdale, PhD1
Susan Elliott, PhD1
Samantha Meyer, PhD1
Shannon Majowicz, PhD1

1- School of Public Health and Health Systems, University of Waterloo, Waterloo, ON Canada.

### Report funded by:

This study was supported by a grant from the Dairy Farmers of Canada (PI: S.E. Majowicz), the Canadian Foundation for Innovation, and Canada Research Chairs. This study analysed data from the COMPASS system, the development of which was supported by a bridge grant from the Canadian Institutes of Health Research (CIHR) Institute of Nutrition, Metabolism and Diabetes through the “Obesity – Interventions to Prevent or Treat” priority funding awards (OOP-110788; grant awarded to ST. Leatherdale) and an operating grant from the CIHR Institute of Population and Public Health (MOP-114875; grant awarded to ST. Leatherdale). The COMPASS expansion to additional jurisdictions was funded by a Health Canada grant through the Substance Use and Addictions Program (SUAP). Dr. Leatherdale is a Chair in Applied Public Health Research funded by the Public Health Agency of Canada in partnership with CIHR. The authors would also like to acknowledge the schools and school boards who participated in the 2017-2018 COMPASS data collections.

### Conflicts of interest:

This study was supported by a grant from the Dairy Farmers of Canada (DFC).  DFC did not have any role in the design nor analysis of this research. Under the conditions of this grant, the authors reserve the right to publish the results of the study and have the final decision on the content of papers and the journal(s) to which the papers are submitted (although DFC encourages publication in Canadian journals or those with a Canadian readership).  As per the conditions of the grant, the researchers provided DFC with a copy of this manuscript before its submission; DFC did not provided comments on the manuscript.  SEM has served as a paid Expert on behalf of the Attorney General of Canada in legal proceedings, providing evidence on the public health risks and benefits of unpasteurized milk.

### Suggested citation:

Butler A, Battista K, Leatherdale ST, Elliott S, Meyer S, Majowicz S.  Correlates of milk and milk alternative consumption among Canadian secondary school students, circa 2017/2018: Technical Report Series. (2020); 7 (1): Waterloo, Ontario: University of Waterloo.

### Contact:

COMPASS Research Team
University of Waterloo
200 University Ave West, BMH 1038
compass@uwaterloo.ca

## Introduction

Adolescence is a critical time when individuals establish autonomy and adopt lasting health behaviors.(1) Poor diet developed during adolescence is typically maintained into adulthood (2) and is associated with a number of chronic diseases such as cardiovascular disease, type-II Diabetes mellitus, cancer, and bone disease.(3)  Understanding factors that influence diet among adolescents is therefore essential for informing programs and policies designed to promote healthy eating, and ultimately, for preventing future chronic disease burdens.

One of the major food groups traditionally considered important for a healthy diet is milk and milk alternatives (MMAs), in part because adequate calcium and vitamin D intake are considered vital for lifelong bone health.(4–7) However, MMA consumption is declining, with an overall decrease since 2009,(8) and a decline that appears to begin in adolescence.(9) Although higher MMA consumption appears to be associated with factors such as higher household income,(8) lower BMI and body fat percentage (10), and healthy school environments,(11) MMA consumption patterns among Canadian secondary school students have not been fully explored.

In January 2019, Canada released the first amendment to its food guide since 2007. The 2007 version (most recently published as the 2011 “Eating Well with Canada’s Food Guide” (12)) recommended a minimum number of servings from four main food groups: fruits and vegetables, milk and milk alternatives, meat and meat alternatives, and grains.  In contrast, the 2019 food guide (13) takes a holistic approach to healthy eating, for example recommending consumption of a variety of healthy foods each day, but does not explicitly recommend specific number of servings by food groups. Within this shifting landscape of food policy in Canada, the importance of developing and evaluating healthy eating habits among youth remains a public health priority.

The objective of this research was to quantify the number of MMAs consumed by students, and identify demographic and behavioural characteristics associated with MMA consumption, using the food guide in place at the time of data collection. Specifically, we examined characteristics that were associated with both the number of servings of MMAs consumed, and also with whether students consumed the recommended number of servings of MMAs as outlined in the 2011 Canada food guide.

## Methods

### 2.1 Data source and sample

COMPASS is a prospective cohort study (2012-2021) designed to measure the impact of policy, programs, and resources on various youth health behaviours. Data collected include student, school and broader environment level data from participating secondary schools across Canada. This study analyzed the most recent wave of student-level data (2017-2018; prior to the 2019 changes made to Canada’s food guide), from participating secondary schools in the Canadian provinces of Quebec (n=37), Ontario (n=61), Alberta (n=8) and British Columbia (n=16). A complete description of the COMPASS methodology is available in print (14) and online (www.compass.uwaterloo.ca). COMPASS received ethics approval from a University of Waterloo Human Research Ethics Committee (ORE #: 30118) and all participating school boards; this analysis was conducted under that approval.

Data were collected from 66,434 grade 9-12 students attending 122 secondary schools that participated in the study. Students were purposefully recruited using an active-information passive-consent protocol (14) to ensure anonymity and response rate, and could refuse participation at any time. During class time, students completed the COMPASS student questionnaire,(14) a tool used to measure self-reported demographics, health behaviours and outcomes. Given 19 correlates were included in this exploratory analysis, we clustered similar correlates into five groups to improve the clarity and interpretation of the findings: demographics (5 variables), weight perception (2 variables), physical activity (2 variables), eating behaviours (7 variables), and substance use (3 variables).

### 2.2 Measures

#### Outcome: milk and milk alternatives (MMAs)

The outcome measure for this study, MMA consumption, was measured by asking students “Yesterday, from the time you woke up until the time you went to bed, how many servings of milk and milk alternatives did you have? One ‘food guide’ serving of milk or milk alternatives includes milk, fortified soy beverage, reconstituted powder milk, canned milk, yogurt or kefir (another type of cultured milk products), and cheese”. The survey also included an image, excerpted from the 2011 Canada food guide ((12); in place during the time of data collection), of the types of MMAs and descriptions of serving sizes to assist participants with determining the number of servings consumed. Daily MMA servings were measured as continuous, and response options included: none, 1 serving, 2 servings, 3 servings, 4 servings, 5 servings and 6 or more servings. Students were classified as meeting the guidelines for MMAs if they consumed at least 3 or more servings (the recommendation for youth between the ages of 14 and 18 (12)); those consuming 2 or fewer servings of MMAs were classified as not meeting the guidelines. Moderately high test-retest reliability (ICC 0.69) and fair validity (ICC 0.60) has been observed for the self-reported MMA consumption measure in the student questionnaire.(14)

#### Demographics

Within the student questionnaire, students reported their gender (female, male); ethnicity (White, Black, Asian, First Nations, Métis, Inuit, Latin American or Hispanic, Mixed/Other); and weekly spending money ($0,$1-$5,$6-$10,$11-$20,$21-$40,$41-$100, More than$100, Don’t know). Grade (9, 10, 11, 12) was used as a proxy for age given its relevance to school stakeholders and application of research findings for school-based program implementation.

#### Weight perception

Consistent with other national youth surveillance systems (15,16), BMI was measured via the survey using self-reported height and weight. Students were categorized as underweight, healthy weight, overweight, obese, and unknown in accordance with the International Obesity Task Force BMI classification system.(17) High reliability (ICC 0.95) and substantial validity (ICC 0.84) has been observed for the self-reported BMI measure in the student questionnaire.(18) Additionally, self-reported weight goals were measured by asking students to report: “What are you trying to do with your weight”. Available answers were: “lose weight”, “gain weight”, “stay the same weight” and “do nothing”.

#### Physical activity

Students were asked to report how many minutes of moderate and hard physical activity they engaged in over the past 7 days to assess whether students met the physical activity guidelines. Based on  Canadian 24-hour movement guidelines (19), students were classified as being physically active if they reported 60 minutes of moderate and hard physical activity daily over the past 7 days. Students who reported less than 60 minutes of daily physical activity were categorized as not physically active. All measures have been previously validated.(16,18) We also measured team sport and intramural involvement by asking students: “Do you participate in competitive school sports teams that compete against other schools? (e.g., junior varsity or varsity sports),”; “Do you participate in league or team sports outside of school?”, and; “do you participate in before-school, noon hour, or after-school physical activities organized by your school?”. All three variables were combined to represent overall sports participation. Students reporting “yes” to at least one of the three questions were classified as participating on a sports team. Students that reported “no” to all three questions were classified as not being involved.

#### Eating behaviours

Students were asked to report how many minutes of moderate and hard physical activity they engaged in over the past 7 days to assess whether students met the physical activity guidelines. Based on  Canadian 24-hour movement guidelines (19), students were classified as being physically active if they reported 60 minutes of moderate and hard physical activity daily over the past 7 days. Students who reported less than 60 minutes of daily physical activity were categorized as not physically active. All measures have been previously validated.(16,18) We also measured team sport and intramural involvement by asking students: “Do you participate in competitive school sports teams that compete against other schools? (e.g., junior varsity or varsity sports),”; “Do you participate in league or team sports outside of school?”, and; “do you participate in before-school, noon hour, or after-school physical activities organized by your school?”. All three variables were combined to represent overall sports participation. Students reporting “yes” to at least one of the three questions were classified as participating on a sports team. Students that reported “no” to all three questions were classified as not being involved.

#### Substance use behaviours

For all substance use items, students were categorized as current and non-current users, consistent with previous research.(20) Current smokers were classified as students who reported smoking one or more cigarettes in the past month, current binge drinkers were classified as students who reported having five drinks of alcohol or more on one occasion at least once in the last month, and current cannabis users were classified as students who reported having used cannabis in the last month.

### 2.3 Data analysis

Of the 66,434 students that participated in the student questionnaire, 49,486 had complete data on the number of MMA servings consumed, and the analysis was completed on these students. Mean MMA consumption was calculated for sub-groups of students by characteristic; characteristics of students who met the food guide requirements versus students who did not were compared using χ2 tests. Logistic regression analyses were conducted to evaluate associations between student characteristics and the odds of meeting the guidelines for MMA consumption. Linear regressions were performed to examine associations between the number of daily MMA servings consumed, and student characteristics. Given the large sample size of this study, a p value of ≤ 0.01 was used to determine statistical significance in order to reduce the probability of type I error. Regression models adjusting for school-level clustering were also run; since these models produced similar results (suggesting minimal school-level variability) and there is no universal measure of absolute fit of models controlling for clustering, non-clustered models were reported here to illustrate overall model fit. We used SAS 9.4 (21) to conduct all analyses. For both the logistic and linear regression models, all first order interactions were run between all the other variables and (i) grade and (ii) gender.

## Results

### 3.1 Sample characteristics

Overall, 66.9% of the sample did not meet Canada’s 2011 guidelines for MMA consumption, and on average, students consumed 2.03 [SD: 1.41] servings of MMAs a day (Table 1). Students in Alberta and British Columbia consumed a lower average number of servings of MMAs a day than students in Ontario and Quebec. Students who met the MMA guidelines more commonly reported being male (61.5% versus 38.5%) and males also consumed a higher daily number of servings (2.36 [SD: 1.47]) than females (1.73 [SD: 1.27]). As grade-level increased, the mean number of servings of MMAs consumed decreased. Students who identified as white consumed the highest mean number of servings per day (2.14 [SD: 1.42]) compared to all other reported ethnicities. Students who reported having any amount of spending money or did not know how much spending money was available to them consumed a higher daily number of MMA servings than students who had no spending money. Students who reported a BMI that was underweight (2.09 [SD: 1.45]) or obese (2.14 [SD: 1.48]) consumed a higher daily number of MMA servings than students who were a healthy weight (2.07 [SD: 1.39]), overweight (2.05 [SD: 1.42]) or had an unknown BMI (1.89 [SD: 1.40]). Students reporting trying to lose weight consumed fewer mean servings of MMAs compared to students who were trying to gain weight, maintain their weight, or who had no weight goals. Physically active students had a higher daily number of MMA servings (2.25 [SD: 1.46]) than students who did not participate in physical activity (1.89 [SD: 1.35]). Students involved in organized sport consumed higher mean servings of MMAs per day (2.18 [SD: 1.42] vs 1.81 [SD: 1.40]) and more commonly met the MMA guidelines than students who did not participate in sports. Moreover, students who met Canada’s 2011 food guide for the other food groups, brought their lunch from home, ate breakfast every day, and purchased items from the vending machines all consumed higher mean daily servings of MMAs than students who did not. Students who were current smokers or binge drinkers consumed lower mean daily servings of MMAs than students who were not, whereas students who were current cannabis users consumed higher mean daily servings of MMAs than students who were not.